Periodic signal extraction of GNSS height time series based on adaptive singular spectrum analysis

Singular spectrum analysis is widely used in geodetic time series analysis.However,when extracting time-varying periodic signals from a large number of Global Navigation Satellite System(GNSS)time series,the selection of appropriate embedding window size and principal components makes this method cumbersome and inefficient.To improve the efficiency and accuracy of singular spectrum analysis,this paper proposes an adaptive singular spectrum analysis method by combining spectrum analysis with a new trace matrix.The running time and correlation analysis indicate that the proposed method can adaptively set the embedding window size to extract the time-varying periodic signals from GNSS time series,and the extraction efficiency of a single time series is six times that of singular spectrum analysis.The method is also accurate and more suitable for time-varying periodic signal analysis of global GNSS sites.

Modeling a Periodic Signal Using Fourier Series

This paper covers the concept of Fourier series and its application for a periodic signal. A periodic signal is a signal that repeats its pattern over time at regular intervals. The idea inspiring is to approximate a regular periodic signal, under Dirichlet conditions, via a linear superposition of trigonometric functions, thus Fourier polynomials are constructed. The Dirichlet conditions, are a set of mathematical conditions, providing a foundational framework for the validity of the Fourier series representation. By understanding and applying these conditions, we can accurately represent and process periodic signals, leading to advancements in various areas of signal processing. The resulting Fourier approximation allows complex periodic signals to be expressed as a sum of simpler sinusoidal functions, making it easier to analyze and manipulate such signals.

Weak wide-band signal detection method based on small-scale periodic state of Duffing oscillator

The conventional Duffing oscillator weak signal detection method, which is based on a strong reference signal, has inherent deficiencies. To address these issues, the characteristics of the Duffing oscillator＇s phase trajectory in a small- scale periodic state are analyzed by introducing the theory of stopping oscillation system. Based on this approach, a novel Duffing oscillator weak wide-band signal detection method is proposed. In this novel method, the reference signal is discarded, and the to-be-detected signal is directly used as a driving force. By calculating the cosine function of a phase space angle, a single Duffing oscillator can be used for weak wide-band signal detection instead of an array of uncoupled Duffing oscillators. Simulation results indicate that, compared with the conventional Duffing oscillator detection method, this approach performs better in frequency detection intervals, and reduces the signal-to-noise ratio detection threshold, while improving the real-time performance of the system.

DETECTING TECHNIQUE OF WEAK PERIODIC PULSE SIGNAL VIA SYNTHESIS OF CROSS-CORRELATION AND CHAOTIC SYSTEM

In this letter, with the synthesis of usual cross-correlation detecting method andchaotic detecting method, a new detecting system for the weak periodic pulse signal is constituted,in which the two methods can play respective preponderance. Theoretical analyses and simulationstudies have shown that the detecting system is very sensitive to the periodic pulse signal understrong noise background and has exceedingly powerful capability of suppressing complex noise.

THE CHAOTIC DETECTION OF PERIODIC SHORT-IMPULSE SIGNALS UNDER STRONG NOISE BACKGROUND

The periodic short-impulse signals under strong noise background are successfully detected with a special chaotic system invented by the authors. Simulation experiments show that the chaotic system is very sensitive to periodic short-impulse signals submerged by strong noise background, and it can effectively restrain any zero-mean noise. The system has a stable working-detection limit of -83dB.

Research and application of regular phenomenon between periodic signals

The phase change between periodic signals is regular. Research on the regular phenomenon between periodic signals is helpful to improve the precision of some measurements and develop some new measurement methods. So it is necessary to analyze the characteristics of the greatest common factor frequency and the least common multiple period universally existing in periodic signals. The regulation of the quantitative phase shift resolution between periodic signals is presented.The cause of difference in phase characteristics between periodic signals is explained well. In this paper we propose different application prospects based on the regular phenomenon between periodic signals, with focusing on the methods for high precision frequency measurement and transient stability measurement. The experimental results are satisfactory.

Stochastic resonance in a time-delayed asymmetric bistable system with mixed periodic signal

This paper studies the phenomenon of stochastic resonance in an asymmetric bistable system with time-delayed feedback and mixed periodic signal by using the theory of signal-to-noise ratio in the adiabatic limit. A general approximate Fokker-Planck equation and the expression of the signal-to-noise ratio are derived through the small time delay approximation at both fundamental harmonics and mixed harmonics. The effects of the additive noise intensity Q, multiplicative noise intensity D, static asymmetry r and delay time T on the signal-to-noise ratio are discussed. It is found that the higher mixed harmonics and the static asymmetry r can restrain stochastic resonance, and the delay time τ can enhance stochastic resonance. Moreover, the longer the delay time τ is, the larger the additive noise intensity Q and the multiplicative noise intensity D are, when the stochastic resonance appears.

Wavelet threshold method of resolving noise interference in periodic short-impulse signals chaotic detection

The chaotic oscillator has already been considered as a powerful method to detect weak signals, even weak signals accompanied with noises. However, many examples, analyses and simulations indicate that chaotic oscillator detection system cannot guarantee the immunity to noises （even white noise）. In fact the randomness of noises has a serious or even a destructive effect on the detection results in many cases. To solve this problem, we present a new detecting method based on wavelet threshold processing that can detect the chaotic weak signal accompanied with noise. All theoretical analyses and simulation experiments indicate that the new method reduces the noise interferences to detection significantly, thereby making the corresponding chaotic oscillator that detects the weak signals accompanied with noises more stable and reliable.

Periodic electromagnetic signals as potential precursor for seismic activity

Electromagnetic signals may be a promising precursor to seismic activity which has been observed in many case studies in past decades.However,the correlation and causation between the electromagnetic signals and the seismic activity are still unclear without intensive observation network.In order to find seismoelectromagnetic phenomenon,we deployed AETA(acoustic and electromagnetic testing all-in-one system),a high-density multi-component seismic monitoring system in the China Earthquake Science Experiment site(CESE,in Sichuan Province and Yunnan Province,China)and the capital circle(areas with a distance which is≤200 km from Beijing),to record electromagnetic and geo-acoustic data across 0.1 Hz−10 kHz.In the course of data collection,we discovered an electromagnetic waveform that occurs on a daily basis.Because the signal generally coincides with sunrise and sunset,we named this phenomenon the SRSS(Sunrise-Sunset)waveform.After conducting three statistical tests based on seismicity and SRSS,we determined that the SRSS waveform is roughly correlated with the onset of seismic activity.It generally occurs at the regions where seismicity occurs.This discovery might have significant implications with respect to the future of earthquake prediction.

LOOP-SHIFT S&G ADAPTIVE FILTER AND ITS APPLICATION IN SEPARATION OF OVERLAPPED PERIODIC BIOMEDICAL SIGNALS

A Loop-Shift Stop & Go Adaptive Filter(LSSGAF) with applications in period detection and signal separation of overlapped periodic signals is presented. The filter places no constraints on the overlapped forms in frequency domain and requires no priori knowledge except for approximate period range of signals to be separated, therefore it provides for an efficient and feasible method to separate periodic signals overlapped both in time and frequency domains. The convergence conditions of the filter and the requirements of all parameter selections are discussed, meanwhile a rough scanning and channel auto-shut algorithm is proposed to reduce the amount of calculation in the searching stage. Experiments show that the filter can separate all kinds of overlapped periodic signals with different initial phases and different relative magnitudes.

Observation 20-s periodic signals on Mars from InSight,Sols 800-1,000

Seismometers of the InSight probe(Interior Exploration using Seismic Investigation,Geodesy and Heat Transport)currently operating on Mars have recorded not only seismic events but also high-frequency non-seismic periodic signals that appear to have been induced by variations in the Martian environment and the hardware.Here,we report an observation of a long-period signal with a dominant period of~20 s from Martian solar days(Sol)800 to Sol 1,000.This 20-s signal is detected mostly at quiet nighttime—from22:00 to 04:00 LMST(Local Mean Solar Time)—at the InSight landing site.The measurement of the particle motion suggests that this linearly polarized signal focuses on the horizontal plane with an angle of~30°from the north.By examining the temporal variation of the signal’s amplitude and polarization angle and its times of occurrence in relation to the planet’s atmospheric data,we suggest that this20-s signal may be relevant to wind and temperature variations on Mars.Furthermore,we study the possible influence of this 20-s signal on the noise autocorrelation and find that the stacked autocorrelograms can be quite different when the 20-s signal is present.

Noise reduction and periodic signal extraction for GNSS height data in the study of vertical deformation

Global navigation satellite system(GNSS)technique has irreplaceable advantages in the continuous monitoring of surface deformation.Reducing noise to improve the signal-to-noise ratio(SNR)and extract the concerned signals is of great significance.As an improved algorithm of empirical mode decomposition(EMD),complete ensemble empirical mode decomposition with adaptive noise(CEEMDAN)algorithm has better signal processing ability.Using the CEEMDAN algorithm,the height time series of 29GNSS stations in Chinese mainland were analyzed,and good denoising effects and extraction from periodic signals were achieved.The numerical results showed that the annual signal obtained with the CEEMDAN algorithm was significantly based on Lomb_Scargle spectrum analysis,and large differences in the long-term signals were found between the stations at different locations in Chinese mainland.With respect to data denoising,compared with the EMD and wavelet denoising algorithms,the CEEMDAN algorithm respectively improved the SNR by 29.35% and 36.54%,increased the correlation coefficient by 8.67% and 11.96%,and reduced root mean square error(RMSE)by 44.68% and 43.48%,indicating that the CEEMDAN algorithm had better denoising behavior than the other two algorithms.In addition,the results demonstrated that different denoising methods had little influence on estimating the annual vertical deformation velocity.The extraction of periodic signals showed that more components were retained by using the CEEMDAN algorithm than the EMD algorithm,which indicated that the CEEMDAN algorithm had advantages over frequency aliasing.In conclusion,the CEEMDAN algorithm was recommended for processing the GNSS height time series to analyze the vertical deformation due to its excellent features of denoising and the extraction of periodic signals.

Parameter estimation method for a linear frequency modulation signal with a Duffing oscillator based on frequency periodicity

In view of the complexity of existing linear frequency modulation(LFM)signal parameter estimation methods and the poor antinoise performance and estimation accuracy under a low signal-to-noise ratio(SNR),a parameter estimation method for LFM signals with a Duffing oscillator based on frequency periodicity is proposed in this paper.This method utilizes the characteristic that the output signal of the Duffing oscillator excited by the LFM signal changes periodically with frequency,and the modulation period of the LFM signal is estimated by autocorrelation processing of the output signal of the Duffing oscillator.On this basis,the corresponding relationship between the reference frequency of the frequencyaligned Duffing oscillator and the frequency range of the LFM signal is analyzed by the periodic power spectrum method,and the frequency information of the LFM signal is determined.Simulation results show that this method can achieve high-accuracy parameter estimation for LFM signals at an SNR of-25 dB.

Joint observation of long period tremor signals with broadband seismometer,tiltmeter and gravimeter

We report here the observation result of joint observation of long period tremor signals with broadband seismome-ter,tiltmeter and gravimeter at the HUST(Huazhong University of Science and Technology)station.The observed data were compared and analyzed.Since 2005,the several tens of abnormal tremor signals which are weak,com-plex and duration of 2 to 3 days have been synchronously recorded by the different instruments.The tremor signals have the periodic domain in the range of 3 to 5 minutes,20 to 30 minutes and even more than 1 hour.The observa-tion shows such tremors are a physical existence.The analysis indicates that a part of the tremors caused by the typhoon from the western Pacific Ocean.These tremors have a close relationship with wind velocity of typhoon and distance between the typhoon center and the station.Except these,the cause of others is still unclear.

North Atlantic Abrupt Climate Signals during the Last Glacial Period in Central Asia：Evidences from Aeolian Loess Sediments

Objective Climate fluctuations over suborbital or millennial timescale display significant instability during the last glacial period,which are often superimposed upon the orbital periodicity.They triggered some abrupt climate events,

鼻咽癌中Period2下调ERK/MAPK磷酸化水平的机制

目的探讨生物钟基因Period2在鼻咽癌中下调ERK/MAPK磷酸化水平的分子机制。方法用慢病毒对细胞进行感染,构建细胞系。采用Label-free蛋白质组学检测方法对PER2蛋白表达进行验证。采用不同浓度的ERK通路激活剂Ceramide C6干预各组细胞,MTT检测各组细胞的增殖能力,筛选最佳药物浓度和作用时间。采用ERK通路激活剂Ceramide C6干预细胞,正常对照组采用等剂量药物溶剂进行处理,分为6组。Western blot检测各组细胞PER2、ERK、p38MAPK、p-ERK、p-p38MAPK蛋白的表达。流式细胞术检测细胞周期,Transwell实验检测各组细胞侵袭能力,进一步明确PER2调控ERK、MAPK磷酸化的机制。用免疫组化检测PER2、p-ERK在人体鼻咽癌样本中的表达,并分析PER2、p-ERK与鼻咽癌临床特点的相关性。结果蛋白质组学检测结果示PER2过表达组的PER2蛋白表达明显高于阴性病毒对照组和空白对照组(P<0.05)。Top 20差异蛋白显示MAPK3在PER2过表达组和对照组差异有统计学意义(P<0.05)。MTT实验结果:不同药物浓度处理后,根据每组细胞抑制率,得出最佳的药物作用浓度10μmol/L,最佳处理时间24 h。WB结果显示PER2过表达下调p-ERK、p-p38MAPK蛋白表达,ERK通路激活剂Ceramide C6干预后,在PER2-OE组中ERK、p38MAPK蛋白水平无明显变化,但p-ERK、p-p38MAPK蛋白表达水平上调,干预前后差异有统计学意义(P<0.01)。细胞周期实验结果显示:ERK通路激活剂Ceramide C6干预各组细胞后,使细胞在G1期的比例明显增加,而在G2期数量减少。Transwell实验结果显示PER2过表达抑制细胞侵袭能力,此种现象不能被磷酸激酶逆转。在鼻咽癌组织、鼻咽部黏膜中PER2蛋白、p-ERK蛋白表达阳性率差异有统计学意义(P<0.05)。PER2蛋白表达与肿瘤的T分期相关(P<0.05)。在鼻咽癌组织中PER2蛋白与p-ERK蛋白表达相关(P<0.05)。结论PER2过表达下调ERK/MAPK磷酸化水平,可能不是直接作用于ERK和p38MAPK的磷酸化位点,而是与调控ERK和p38MAPK的关键节点有关。鼻咽癌组织中PER2蛋白呈低表达,p-ERK蛋白呈高表达,两者相关,并且与肿瘤的发生、发展关系密切。

LPI radar signal detection based on the combination of FFT and segmented autocorrelation plus PAHT

This paper proposes a desirable method to detect different kinds of low probability of intercept (LPI) radar signals, targeted at the main intra-pulse modulation method of LPI radar signals including the signals of linear frequency modulation, phase code, and frequency code. Firstly, it improves the coherent integration of LPI radar signals by adding the periodicity of the ambiguity function. Then, it develops a frequency domain detection method based on fast Fourier transform (FFT) and segmented autocorrelation function to detect signals without features of linear frequency modulation by virtue of the distribution characteristics of noise signals in the frequency domain. Finally, this paper gives a verification of the performance of the method for different signal-to-noise ratios by conducting simulation experiments, and compares the method with existing ones. Additionally, this method is characterized by the straightforward calculation and high real-time performance, which is conducive to better detecting all kinds of LPI radar signals.

Detection of different-time-scale signals in the length of day variation based on EEMD analysis technique

Scientists pay great attention to different-time-scale signals in the lengllh of day （LOD） variations △LOD, which provide signatures of the Earth＇s interior structure, couplings among different layers, and potential excitations of ocean and atmosphere. In this study, based on the ensemble empirical mode decomposition （EEMD）, we analyzed the latest time series of △LOD data spanning from January 1962 to March 2015. We observed the signals with periods and amplitudes of about 0.5 month and 0.19 ms, 1.0 month and 0.19 ms, 0.5 yr and 0.22 ms, 1.0 yr and 0.18 ms, 2.28 yr and 0.03 ms, 5.48 yr and 0.05 ms, respectively, in coincidence with the results of predecessors. In addition, some signals that were previously not definitely observed by predecessors were detected in this study, with periods and amplitudes of 9.13 d and 0.12 ms, 13.69 yr and 0.10 ms, respectively. The mechanisms of the LOD fluctuations of these two signals are still open.

Analysis of a kind of Duffing oscillator system used to detect weak signals

The stability of the periodic solution of the Duffing oscillator system in the periodic phase state is proved by using the Yoshizaw theorem, which establishes a theoretical basis for using this kind of chaotic oscillator system to detect weak signals. The restoring force term of the system affects the weak-signal detection ability of the system directly, the quantitative relationship between the coefficients of the linear and nonlinear items of the restoring force of the Duffing oscillator system and the SNR in the detection of weak signals is obtained through a large number of simulation experiments, then a new restoring force function with better detection results is established.

Effects of Electric Stimulations Applied During Absolute Refractory Period on Ventricular Muscle from Rabbits

Objectives To investigate the influences of electric signals applied during absolute refractory period (ARP) on the contractility of isolated papillary muscle from rabbits. Methods Papillary muscle was exercised from the right ventricle and was paced at 1 Hz. Biphasic square wave current pulse was delivered during the absolute refractory period (called CCM) in isolated, superfused, isometrically contractility rabbit papillary muscle. The peak tension (PT) of papillary muscle, as well as maximum positive tension change ( + dT/dtmax), were observed. Results Compared with the baseline, both PT and + dT/dtmax significantly increased during CCM stimulation by 18.2% and 21.4% respectively (P < 0. 05) . In addition, PT increased significantly with one or two beats following CCM signal application and reached a. new steady state level after a few beats. Once the CCM signals were turned off, the PT returned to the approximately baseline level ( P < 0. 05). Moreover, the effect of CCM on PT was dose - response to voltage. The obvious effect was at higher voltage. No effect was observed at lower voltage. Conclusions Electric signals delivered during the absolute refractory period can rapidly enhance the contractility of myocardium, which suggests that CCM signal is a novel potent method for contractility modulation.